Equipment intelligent operation and maintenance management method based on fault knowledge base

Abstract

The invention relates to the technical field of equipment operation and maintenance management, and discloses an intelligent equipment operation and maintenance management method based on a fault knowledge base, and the method comprises the steps: collecting equipment data, storing the equipment data in a data warehouse, carrying out the preprocessing of the equipment data, and guaranteeing the centralized management and easy access of the equipment data; a fault knowledge base is constructed based on historical equipment data to help understand different types of faults and reasons thereof, and support is provided for diagnosis of fault states; a state monitoring model is constructed based on historical equipment data and real-time equipment data, the fault state of the equipment is identified, potential problems can be found in time, and the fault state is prevented; if the equipment is in a fault state, the fault position is diagnosed based on the fault knowledge base and real-time equipment data, and maintenance measures are put forward, so that the maintenance time is shortened, and the equipment reliability is improved; and continuously monitoring the fault state of the equipment after maintenance, and optimizing the fault knowledge base and maintenance measures.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment operation and maintenance management, and in particular to an equipment intelligent operation and maintenance management method based on a fault knowledge base. Background Art

[0002] In the existing technology, machine learning and big data analysis are used to predict equipment failures, realize automatic diagnosis and prediction of equipment failures, realize real-time monitoring and adjustment of equipment status, build a fault knowledge base, including fault modes, solutions, etc., provide real-time problem-solving support for operation and maintenance personnel, realize intelligent operation and maintenance decision-making based on big data analysis, and improve equipment reliability and efficiency. However, there is no solution to how to collect equipment data to build a fault knowledge base and status monitoring model to identify the fault status and fault location of the equipment, and monitor whether the fault status exists after the fault location is repaired.

[0003] For example, the patent with publication number CN117474525A discloses a collaborative operation and maintenance method based on the entire life cycle of shield TBM equipment. This method integrates and processes the data of the entire life cycle of equipment design, manufacturing, production, operation, and maintenance, and performs data mining and analysis through statistical analysis, classification analysis, correlation analysis, machine learning and other algorithms, to provide all-round support for the operation and maintenance of shield TBM engineering projects. This method can realize comprehensive intelligent management and control of shield TBM engineering tunnels, geology, equipment, business information, etc., and provide operation optimization, posture control, abnormal warning, parameter warning and other operation decisions during the shield TBM excavation process; through the evaluation and analysis of equipment excavation efficiency, key parameters, excavation procedures, etc., the equipment excavation efficiency is effectively guaranteed; through collaborative maintenance, the management and guidance of operation maintenance, shutdown maintenance, overhaul maintenance, etc. are realized to speed up the efficiency of repair and maintenance, ensure the normal operation of equipment, and improve the ability to prevent and control risks.

[0004] For example, a Chinese patent with authorization announcement number CN113254541B discloses a theater operation and maintenance management method based on cluster analysis, including: using equipment browsing volume X and time period T as the main components to establish attention; clustering attention time according to the same time period of different days to obtain N data clusters of different equipment; using equipment maintenance volume Y and time period T as the main components to establish maintenance degree; clustering maintenance time according to the same time period of different days to obtain M data clusters of different equipment; performing comparative analysis on N data clusters of attention time clustering and M data clusters of maintenance time clustering; and realizing targeted operation and maintenance management of equipment according to the analysis results. The above technical solution uses the behavior and maintenance record data of relevant equipment in the browsing platform of the operation and maintenance management personnel as the data warehouse, and realizes the effective management of theater performing arts equipment by the operation and maintenance management personnel by combining the clustering grouping results with historical judgment, optimizes the operation and maintenance management data, simplifies the operation and maintenance management behavior, and realizes efficient and accurate operation and maintenance.

[0005] The above patents have the problems raised by this background technology: the above-mentioned collaborative operation and maintenance method for the entire life cycle of equipment integrates and processes the data of the entire life cycle of equipment design, manufacturing, production, operation, and maintenance, and performs data mining and analysis through statistical analysis, classification analysis, correlation analysis, machine learning and other algorithms, so as to provide comprehensive intelligent management and control of shield TBM engineering tunnels, geology, equipment, business information, etc.; the above-mentioned theater operation and maintenance management method uses the equipment browsing volume X and time period T as the main components to establish the attention degree, and uses the equipment maintenance volume Y and time period T as the main components to establish the maintenance degree, and clusters the attention time and maintenance time according to the same time period of different days to obtain N data clusters and M data clusters of different equipment, and compares and analyzes the N data clusters of the attention time cluster and the M data clusters of the maintenance time cluster to realize targeted operation and maintenance management of the equipment. The above-mentioned patents do not solve the problem of how to identify the fault state and fault location of the equipment by collecting equipment data to build a fault knowledge base and a state monitoring model, and monitor whether there is a fault state after the fault location is repaired. To solve this problem, the present invention proposes an intelligent operation and maintenance management method for equipment based on a fault knowledge base. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the above problems existing in the existing equipment intelligent operation and maintenance management method based on the fault knowledge base, the present invention is proposed.

[0008] Therefore, the object of the present invention is to provide an intelligent operation and maintenance management method for equipment based on a fault knowledge base.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] The equipment intelligent operation and maintenance management method based on the fault knowledge base includes:

[0011] Collect equipment data, store the equipment data in the data warehouse, and pre-process the equipment data;

[0012] Build a fault knowledge base based on historical equipment data;

[0013] Build a condition monitoring model based on historical equipment data and real-time equipment data to identify equipment failure conditions;

[0014] If the equipment fails, diagnose the fault location and propose maintenance measures based on the fault knowledge base and real-time equipment data;

[0015] Continuously monitor the fault status of equipment after maintenance, and optimize the fault knowledge base and maintenance measures.

[0016] As a preferred solution of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention, the construction strategy of the fault knowledge base includes:

[0017] Determine the entities and relationships of the fault knowledge base, fill the entities of the fault knowledge base based on historical equipment data, determine the relationships between the entities, configure the relationship threshold, and if the degree of relationship between the entities is less than the relationship threshold, replace the relationship between the entities;

[0018] If the degree of relationship between entities is greater than or equal to the relationship threshold, the relationship between the entities remains unchanged.

[0019] As a preferred solution of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention, the state monitoring model includes a normal identification strategy and a fault detection strategy. The normal identification strategy includes learning the normal state of the equipment through a normal identification formula. The calculation of the normal identification formula is as follows:

[0020] y i =g(w2×(f(w1×x i +b1))+b2);

[0021] In the formula, y i represents the recognition result of the i-th historical equipment data output, g(·) represents the activation function from the hidden layer to the output layer, w2 represents the weight coefficient from the hidden layer to the output layer, f(·) represents the activation function from the input layer to the hidden layer, w1 represents the weight coefficient from the input layer to the hidden layer, x i represents the input i-th historical equipment data, b1 represents the bias coefficient from the input layer to the hidden layer, and b2 represents the bias coefficient from the hidden layer to the output layer;

[0022] The accuracy of the recognition result output by the normal recognition formula is judged by the loss function. The function expression of the loss function is as follows:

[0023]

[0024] Where L represents the loss value of the recognition result, n represents the total number of historical equipment data, and ||·|| represents the norm calculation.

[0025] As a preferred solution of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention, the fault detection strategy includes calculating the covariance matrix and mean vector of the historical equipment data, calculating the distance between the real-time equipment data and the normal state data in the historical equipment data, and the distance formula between the real-time equipment data and the normal state data in the historical equipment data is as follows:

[0026]

[0027] Where, d m Indicates the distance between the mth real-time equipment data and the normal state data in the historical equipment data. represents the mth real-time equipment data, μ represents the mean of the normal state data in the historical equipment data, and T represents the The transpose of , ∑ represents the covariance matrix of normal state data in historical equipment data;

[0028] Configure a distance threshold. If the distance between the real-time equipment data and the normal state data in the historical equipment data is less than or equal to the distance threshold, the real-time equipment data is in a normal state.

[0029] If the distance between the real-time equipment data and the normal state data in the historical equipment data is greater than the distance threshold, the real-time equipment data is in a fault state.

[0030] As a preferred solution of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention, if the equipment fails, the fault location is diagnosed through the location diagnosis strategy, and the location diagnosis strategy includes:

[0031] By comparing the real-time equipment data with the fault knowledge base, the fault type of the real-time equipment data in the fault state is identified, and by matching with the fault type in the fault knowledge base, if the fault type of the real-time equipment data in the fault state successfully matches the fault type in the fault knowledge base, the fault cause and equipment component of the real-time equipment data in the fault state are located according to the fault knowledge base;

[0032] If the fault type of the real-time equipment data being in a fault state fails to match the fault type in the fault knowledge base, the fault cause of the real-time equipment data being in a fault state is checked and the fault knowledge base is updated.

[0033] As a preferred solution of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention, wherein: based on the fault knowledge base and real-time equipment data, the fault location is diagnosed, the fault location is repaired according to the maintenance measures, and the fault state of the equipment after maintenance is continuously monitored. If the equipment does not have a fault state after maintenance, the maintenance process is recorded in the fault knowledge base;

[0034] If the equipment is in a fault state after maintenance, the fault location is determined and the fault knowledge base and maintenance measures are updated according to the location diagnosis strategy.

[0035] As a preferred solution of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention, wherein: the equipment data includes equipment status, operating environment and historical fault records;

[0036] The collected equipment data is stored in the data warehouse through storage strategies, which include:

[0037] Determine the update mode of equipment data and configure a frequency threshold. If the update frequency of equipment data is less than or equal to the frequency threshold, the update mode of equipment data is low-frequency update.

[0038] If the update frequency of the equipment data is greater than the frequency threshold, the update mode of the equipment data is high-frequency update;

[0039] Determine the storage mode of equipment data and configure the stock threshold. If the storage amount of equipment data is less than or equal to the stock threshold, the storage mode of equipment data is low-frequency storage.

[0040] If the storage amount of equipment data is greater than the storage threshold, the storage method of equipment data is high-frequency storage;

[0041] The equipment data loading method is comprehensively determined based on the equipment data update method and storage method. If the equipment data update method is low-frequency update and the equipment data storage method is high-frequency storage, the equipment data loading method is full loading.

[0042] If the update mode of equipment data is high-frequency update and the storage mode of equipment data is low-frequency storage, the loading mode of equipment data is incremental loading;

[0043] Dynamically adjust the resource allocation of the data warehouse according to the loading mode of equipment data, and configure the resource quantity, which includes baseline resources and extreme resources. If the loading mode of equipment data is full loading, extreme resources are allocated;

[0044] If the loading method of equipment data is incremental loading, the baseline resources are allocated.

[0045] A computer device includes a memory for storing instructions and a processor for executing the instructions, so that the device implements an intelligent operation and maintenance management method for equipment based on a fault knowledge base.

[0046] A computer-readable storage medium stores a computer program, which, when executed, implements an intelligent equipment operation and maintenance management method based on a fault knowledge base.

[0047] The beneficial effects of the present invention are as follows: the present invention collects equipment data, stores the equipment data in a data warehouse, and pre-processes the equipment data to ensure centralized management and easy access to the equipment data; builds a fault knowledge base based on historical equipment data to help understand different types of faults and their causes, and provide support for the diagnosis of fault conditions; builds a condition monitoring model based on historical equipment data and real-time equipment data to identify the fault condition of the equipment, which helps to promptly discover potential problems and prevent the occurrence of fault conditions; if a fault condition occurs in the equipment, the fault location is diagnosed based on the fault knowledge base and real-time equipment data, and maintenance measures are proposed, which reduces maintenance time and improves equipment reliability; the fault condition of the equipment after maintenance is continuously monitored, and the fault knowledge base and maintenance measures are optimized. Through accurate fault diagnosis and maintenance measures, maintenance resources can be better allocated, unnecessary inspections and maintenance can be avoided, and costs can be saved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0049] Figure 1 It is a method flow chart of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention;

[0050] Figure 2 It is a storage strategy flow chart of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention;

[0051] Figure 3 It is a flow chart of a strategy for constructing a fault knowledge base of the equipment intelligent operation and maintenance management method based on a fault knowledge base of the present invention;

[0052] Figure 4 This is a flow chart of the location diagnosis strategy of the equipment intelligent operation and maintenance management method based on the fault knowledge base of the present invention. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0056] Example 1

[0057] In this embodiment, a method flow chart of an equipment intelligent operation and maintenance management method based on a fault knowledge base is provided, such as Figure 1 As shown in the figure, the equipment intelligent operation and maintenance management method based on the fault knowledge base includes:

[0058] S1. Collect equipment data, store the equipment data in a data warehouse, and pre-process the equipment data.

[0059] Equipment data includes equipment status, operating environment and historical fault records.

[0060] The collected equipment data is stored in the data warehouse through storage strategies. The storage strategies are as follows: Figure 2 As shown, specifically including:

[0061] Determine the update mode of equipment data and configure a frequency threshold. If the update frequency of equipment data is less than or equal to the frequency threshold, the update mode of equipment data is low-frequency update.

[0062] If the update frequency of the equipment data is greater than the frequency threshold, the update mode of the equipment data is high-frequency update.

[0063] Determine the storage mode of equipment data and configure the stock threshold. If the storage amount of equipment data is less than or equal to the stock threshold, the storage mode of equipment data is low-frequency storage.

[0064] If the storage amount of equipment data is greater than the storage threshold, the storage method of the equipment data is high-frequency storage.

[0065] The equipment data loading method is comprehensively determined based on the equipment data update method and storage method. If the equipment data update method is low-frequency update and the equipment data storage method is high-frequency storage, the equipment data loading method is full loading.

[0066] If the update mode of equipment data is high-frequency update and the storage mode of equipment data is low-frequency storage, the loading mode of equipment data is incremental loading;

[0067] Dynamically adjust the resource allocation of the data warehouse according to the loading mode of equipment data, and configure the resource quantity, which includes baseline resources and extreme resources. If the loading mode of equipment data is full loading, extreme resources are allocated;

[0068] If the loading method of equipment data is incremental loading, the baseline resources are allocated.

[0069] Among them, full loading refers to the transmission of all equipment data, and incremental loading refers to the transmission of only the equipment data that has changed since the last loading. Here it can be judged that the loading method of the equipment status and operating environment in the equipment data is incremental loading, while the loading method of the historical fault records in the equipment data is full loading. Extreme resources refer to the need for data warehouse resources when all equipment data needs to be transmitted, and baseline resources refer to the need to provide fewer data warehouse resources when only the equipment data that has changed since the last loading is transmitted. At the same time, when the equipment data is loaded in full load, the storage performance requirements for the data warehouse are lower, and when the equipment data is loaded in incremental load, the storage performance requirements for the data warehouse are higher. Classifying equipment data according to the loading method of equipment data can ensure that the response time when accessing equipment data in the data warehouse can meet business needs, and at the same time can optimize the cost of storing equipment data.

[0070] Check the errors and inconsistencies of the equipment data stored in the data warehouse and correct them, including deleting duplicate data, filling missing values ​​and processing outliers. Through standardization, ensure the consistency of equipment data at different scales. The equipment status includes equipment parameters and fault display. The operating environment includes the temperature, humidity and air quality when the equipment is running. These environmental factors may affect the performance and failure rate of the equipment. The historical fault records include all historical fault events, including the time, type, cause, impact and repair measures of the fault.

[0071] S2. Build a fault knowledge base based on historical equipment data.

[0072] The construction strategy of fault knowledge base is as follows: Figure 3 As shown, specifically including:

[0073] Determine the entities and relationships of the fault knowledge base. The entities of the fault knowledge base include fault types, fault causes, and equipment components. The relationships between the entities in the fault knowledge base include influence, belonging to, and cause. Fill the entities of the fault knowledge base based on historical equipment data, determine the relationships between the entities, and configure the relationship threshold. If the degree of relationship between the entities is less than the relationship threshold, replace the relationship between the entities.

[0074] If the degree of relationship between entities is greater than or equal to the relationship threshold, the relationship between entities remains unchanged. For example, there are many reasons for the failure of pump components, and each reason causes the failure of pump components to a different degree. The reduction or increase of the flow rate of the pump will cause the operating efficiency of the pump to decrease or increase. The working pressure of the pump increases or decreases, which may affect the performance and stability of the pump. The current fluctuation of the pump may indicate that the pump is being disturbed. The pump may cause the expansion of components and the degradation of lubricating oil performance in a high temperature environment, and even cause damage to the internal components of the pump. The pump may cause some components to become brittle or solidify in a low temperature environment, affecting the normal operation of the pump. In a high humidity environment, the internal components of the pump may rust and corrode, reducing the life and performance of the pump. In a low humidity environment, the sealing components of the pump may crack and harden, increasing the risk of leakage of the pump. Here, it is necessary to first determine the degree of failure of the pump components, and then determine whether the cause of the failure causes the occurrence of the fault type or affects the occurrence of the fault type, so as to determine the relationship between the entities. The maintenance measures for different fault types here are sorted and analyzed with reference to the maintenance methods of different faults by experts in the field.

[0075] S3. Build a condition monitoring model based on historical equipment data and real-time equipment data to identify the fault status of the equipment.

[0076] The condition monitoring model includes a normal identification strategy and a fault detection strategy. The normal identification strategy includes learning the normal state of the equipment through a normal identification formula. The calculation of the normal identification formula is as follows:

[0077] y i =g(w2×(f(w1×x i +b1))+b2);

[0078] In the formula, y i represents the recognition result of the i-th historical equipment data output, g(·) represents the activation function from the hidden layer to the output layer, w2 represents the weight coefficient from the hidden layer to the output layer, f(·) represents the activation function from the input layer to the hidden layer, w1 represents the weight coefficient from the input layer to the hidden layer, x i represents the i-th historical equipment data input, b1 represents the bias coefficient from the input layer to the hidden layer, and b2 represents the bias coefficient from the hidden layer to the output layer.

[0079] The accuracy of the recognition result output by the normal recognition formula is judged by the loss function. The function expression of the loss function is as follows:

[0080]

[0081] Where L represents the loss value of the recognition result, n represents the total number of historical equipment data, and ||·|| represents the norm calculation.

[0082] It needs to be explained that the normal identification strategy is to learn the normal status of the equipment through historical equipment data, while the fault detection strategy is to detect faults in real-time equipment data and identify the fault status of the equipment.

[0083] The fault detection strategy includes calculating the covariance matrix and mean vector of historical equipment data, and calculating the distance between real-time equipment data and normal state data in historical equipment data. The distance formula between real-time equipment data and normal state data in historical equipment data is as follows:

[0084]

[0085] Where, d m Indicates the distance between the mth real-time equipment data and the normal state data in the historical equipment data. represents the mth real-time equipment data, μ represents the mean of the normal state data in the historical equipment data, and T represents the is the transpose of , ∑ represents the covariance matrix of normal state data in historical equipment data.

[0086] Configure a distance threshold. If the distance between the real-time equipment data and the normal state data in the historical equipment data is less than or equal to the distance threshold, the real-time equipment data is in a normal state.

[0087] If the distance between the real-time equipment data and the normal state data in the historical equipment data is greater than the distance threshold, the real-time equipment data is in a fault state.

[0088] Combining neural networks with anomaly detection algorithms can effectively build a condition monitoring model to identify the fault state of the pump, and can achieve accurate fault state identification in actual application scenarios.

[0089] S4. If the equipment fails, diagnose the fault location and propose maintenance measures based on the fault knowledge base and real-time equipment data.

[0090] If the equipment fails, the fault location is diagnosed through the location diagnosis strategy. The location diagnosis strategy is as follows: Figure 4 As shown, specifically including:

[0091] By comparing the real-time equipment data with the fault knowledge base, the fault type of the real-time equipment data in the fault state is identified, and by matching with the fault type in the fault knowledge base, if the fault type of the real-time equipment data in the fault state successfully matches the fault type in the fault knowledge base, the fault cause and equipment component of the real-time equipment data in the fault state are located according to the fault knowledge base;

[0092] If the fault type of the real-time equipment data being in a fault state fails to match the fault type in the fault knowledge base, the fault cause of the real-time equipment data being in a fault state is checked and the fault knowledge base is updated.

[0093] Take appropriate maintenance measures according to the type and cause of the fault. Before repairing the pump, emergency measures such as shutdown or power off should be taken immediately according to the type and cause of the fault to avoid more serious losses caused by the expansion of the fault. At the same time, set up necessary warning signs and isolation areas to prevent risks caused by misoperation, ensure the safety of operators, and back up key data before and after the fault occurs.

[0094] Based on the degree of damage and repairability of the pump components, decide whether to perform on-site repairs or replace new equipment parts. Update the preventive maintenance plan based on the type of failure to reduce the probability of future failures. Perform functional tests after completing the repair measures to ensure that all pump components return to normal operation. Record the repair process and results in the fault knowledge base for reference in diagnosing and repairing future fault conditions.

[0095] S5. Continuously monitor the fault status of equipment after maintenance, and optimize the fault knowledge base and maintenance measures.

[0096] Diagnose the fault location based on the fault knowledge base and real-time equipment data, repair the fault location according to the maintenance measures, and continuously monitor the fault status of the equipment after maintenance. If the equipment no longer has a fault status after maintenance, the maintenance process will be recorded in the fault knowledge base;

[0097] If the equipment is in a fault state after maintenance, the fault location is determined and the fault knowledge base and maintenance measures are updated according to the location diagnosis strategy.

[0098] We continuously collect pump failure cases, maintenance manuals and solutions to enrich the fault knowledge base, and carry out regular skills training for maintenance personnel to enhance their ability to apply new technologies and methods. At the same time, we regularly review and optimize the content of the fault knowledge base to ensure that the fault knowledge base reflects the latest maintenance measures.

[0099] Example 2

[0100] In this embodiment, a computer device is provided, including a memory and a processor, the memory is used to store instructions, and the processor is used to execute the instructions, so that the computer device executes the steps of implementing the above-mentioned equipment intelligent operation and maintenance management method based on the fault knowledge base.

[0101] Example 3

[0102] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps of the above-mentioned equipment intelligent operation and maintenance management method based on the fault knowledge base are implemented.

[0103] The computer-readable storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and other media for storing program codes.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. An intelligent equipment operation and maintenance management method based on a fault knowledge base, characterized in that: include: Collect equipment data, store the equipment data in the data warehouse, and pre-process the equipment data; Build a fault knowledge base based on historical equipment data; Build a condition monitoring model based on historical equipment data and real-time equipment data to identify equipment failure conditions; If the equipment fails, diagnose the fault location and propose maintenance measures based on the fault knowledge base and real-time equipment data; Continuously monitor the fault status of equipment after maintenance, and optimize the fault knowledge base and maintenance measures.

2. The equipment intelligent operation and maintenance management method based on the fault knowledge base according to claim 1, characterized in that: The strategies for building the fault knowledge base include: Determine the entities and relationships of the fault knowledge base, fill the entities of the fault knowledge base based on historical equipment data, determine the relationships between the entities, configure the relationship threshold, and if the degree of relationship between the entities is less than the relationship threshold, replace the relationship between the entities; If the degree of relationship between entities is greater than or equal to the relationship threshold, the relationship between the entities remains unchanged.

3. The equipment intelligent operation and maintenance management method based on the fault knowledge base according to claim 2 is characterized in that: The condition monitoring model includes a normal identification strategy and a fault detection strategy. The normal identification strategy includes learning the normal state of the equipment through a normal identification formula. The calculation of the normal identification formula is as follows: <h2 style=";text-align:left;direction:ltr">y<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =g(w2×(f(w1×x<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> +b1))+b2); In the formula, y i represents the recognition result of the i-th historical equipment data output, g(·) represents the activation function from the hidden layer to the output layer, w2 represents the weight coefficient from the hidden layer to the output layer, f(·) represents the activation function from the input layer to the hidden layer, w1 represents the weight coefficient from the input layer to the hidden layer, x i represents the i-th historical equipment data input, b1 represents the bias coefficient from the input layer to the hidden layer, and b2 represents the bias coefficient from the hidden layer to the output layer; The accuracy of the recognition result output by the normal recognition formula is judged by the loss function. The function expression of the loss function is as follows: Where L represents the loss value of the recognition result, n represents the total number of historical equipment data, and ||·|| represents the norm calculation.

4. The equipment intelligent operation and maintenance management method based on the fault knowledge base according to claim 3 is characterized in that: The fault detection strategy includes calculating the covariance matrix and mean vector of historical equipment data, and calculating the distance between real-time equipment data and normal state data in historical equipment data. The distance formula between real-time equipment data and normal state data in historical equipment data is as follows: Where, d m Indicates the distance between the mth real-time equipment data and the normal state data in the historical equipment data. represents the mth real-time equipment data, μ represents the mean of the normal state data in the historical equipment data, and T represents the The transpose of , ∑ represents the covariance matrix of normal state data in historical equipment data; Configure a distance threshold. If the distance between the real-time equipment data and the normal state data in the historical equipment data is less than or equal to the distance threshold, the real-time equipment data is in a normal state. If the distance between the real-time equipment data and the normal state data in the historical equipment data is greater than the distance threshold, the real-time equipment data is in a fault state.

5. The equipment intelligent operation and maintenance management method based on the fault knowledge base according to claim 4 is characterized in that: If the equipment fails, the fault location is diagnosed through the location diagnosis strategy, which includes: By comparing the real-time equipment data with the fault knowledge base, the fault type of the real-time equipment data in the fault state is identified, and by matching with the fault type in the fault knowledge base, if the fault type of the real-time equipment data in the fault state successfully matches the fault type in the fault knowledge base, the fault cause and equipment component of the real-time equipment data in the fault state are located according to the fault knowledge base; If the fault type of the real-time equipment data being in a fault state fails to match the fault type in the fault knowledge base, the fault cause of the real-time equipment data being in a fault state is checked and the fault knowledge base is updated.

6. The equipment intelligent operation and maintenance management method based on the fault knowledge base according to claim 5 is characterized in that: Diagnose the fault location based on the fault knowledge base and real-time equipment data, repair the fault location according to the maintenance measures, and continuously monitor the fault status of the equipment after maintenance. If the equipment no longer has a fault status after maintenance, the maintenance process will be recorded in the fault knowledge base; If the equipment is in a fault state after maintenance, the fault location is determined and the fault knowledge base and maintenance measures are updated according to the location diagnosis strategy.

7. The equipment intelligent operation and maintenance management method based on the fault knowledge base according to claim 6 is characterized in that: Equipment data includes equipment status, operating environment, and historical fault records; The collected equipment data is stored in the data warehouse through storage strategies, which include: Determine the update mode of equipment data and configure a frequency threshold. If the update frequency of equipment data is less than or equal to the frequency threshold, the update mode of equipment data is low-frequency update. If the update frequency of the equipment data is greater than the frequency threshold, the update mode of the equipment data is high-frequency update; Determine the storage mode of equipment data and configure the stock threshold. If the storage amount of equipment data is less than or equal to the stock threshold, the storage mode of equipment data is low-frequency storage. If the storage amount of equipment data is greater than the storage threshold, the storage method of equipment data is high-frequency storage; The equipment data loading method is comprehensively determined based on the equipment data update method and storage method. If the equipment data update method is low-frequency update and the equipment data storage method is high-frequency storage, the equipment data loading method is full loading. If the update mode of equipment data is high-frequency update and the storage mode of equipment data is low-frequency storage, the loading mode of equipment data is incremental loading; Dynamically adjust the resource allocation of the data warehouse according to the loading mode of equipment data, and configure the resource quantity, which includes baseline resources and extreme resources. If the loading mode of equipment data is full loading, extreme resources are allocated; If the loading method of equipment data is incremental loading, the baseline resources are allocated.

8. A computer device, characterized in that: include, A memory for storing instructions; The processor is used to execute the instruction so that the computer device executes the equipment intelligent operation and maintenance management method based on the fault knowledge base as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the equipment intelligent operation and maintenance management method based on the fault knowledge base as described in any one of claims 1 to 7 is implemented.

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